Borrowing it
Nothing to install: this file belongs to Nam-Cheol/namba-ai. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Nam-Cheol/namba-ai/main/.agents/skills/namba-plan/SKILL.mdgit clone --depth 1 https://github.com/Nam-Cheol/namba-aiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nam-cheol/namba-ai/namba-plan)<a href="https://agentmods.dev/skills/nam-cheol/namba-ai/namba-plan"><img src="https://agentmods.dev/badge/skills/nam-cheol/namba-ai/namba-plan/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nam-cheol/namba-ai/namba-plan"><img src="https://agentmods.dev/badge/skills/nam-cheol/namba-ai/namba-plan.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00016 | $0.01160 |
| Opus 5.5 | $0.00006 | $0.00464 |
| Sonnet 5.5 | $0.00003 | $0.00232 |
| Haiku 4.5 | $0.00002 | $0.00116 |
Grade A, and why
namba-plan scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 42 lines — stays where its author put it; the contents beside it link to each section on GitHub.
State effect: mutating workflow entry point. Use help/probe paths read-only, and otherwise expect repository state or GitHub state to change.
Generated instruction contract for this command skill:
- Purpose: keep the role or command scope explicit, bounded, and testable.
- Boundary: honor read-only versus mutating state effects, configured sandbox mode, and assigned file or workflow ownership.
- Required output: report concrete actions, changed paths or artifacts, validation evidence, and pass/fail status or blockers.
- Pass/fail criteria: claim success only when acceptance criteria and configured validation are satisfied; otherwise name the exact blocker and impact.
- Evidence expectations: cite source artifacts such as SPEC files,
.namba/configs, diffs, test output, PR/check links, or generated manifests instead of relying on unsupported assertions. - Security responsibilities: never expose or commit secrets; treat auth, privacy, destructive commands, permission changes, and external network or credential use as security-sensitive.
- Destructive command and escalation policy: do not run destructive commands unless explicitly requested; request approval for privileged, networked, or sandbox-blocked actions only when the active approval mode allows it, and otherwise report the blocker or use a safe non-escalating path.
- Fallback implementer boundary: if a specialist path is unavailable and the main/default implementer takes over, stay within the assigned scope and preserve the same evidence and validation duties.
- Portability: keep durable guidance non-project-specific unless the current repository config or SPEC explicitly provides the project detail.
Use this skill when the user explicitly says $namba-plan, namba plan, or asks to create a new feature SPEC package.
Behavior:
- Before reading project docs, checking Git state, or running the CLI, run the Namba clarification gate on the user's raw request.
- If the request is short, broad, or missing target surface, user flow, scope boundaries, constraints, acceptance criteria, or validation, do not run
namba planyet. Ask 1-3 concise questions first;게시판 만들어줘is the canonical example that must ask questions before planning. - Treat Codex Plan mode as the preferred clarification surface and
namba planas the executor: when native Plan mode choice UI is available, use it to ask the clarification questions before any CLI command. - The Plan mode output must be converted into a refined description shaped as Goal, Scope, Constraints, and Acceptance, then passed to
namba plan "<refined description>". - Repo hooks and skills cannot force Codex to switch modes by themselves; if native Plan mode UI is unavailable in the current session, fall back to concise text questions in chat instead of inventing a CLI wizard.
- Continue the clarification loop across turns until you can restate the request as Goal, Scope, Constraints, and Acceptance. Only then invoke the CLI with the refined description.
- Prefer the installed
namba planCLI when available. - Keep
namba planfor feature-oriented SPEC work; usenamba harnesswhen the request is about reusable agent, skill, workflow, or orchestration scaffolding; use$namba-createwhen the user wants the repo-local skill or custom-agent artifact itself instead of another SPEC. - When repo-managed MCP presets are configured, prefer them for planning context before broader web search; for example, use
context7for library and framework docs,sequential-thinkingfor deeper decomposition, andplaywrightfor browser-verified flows. - Read
.namba/project/product.md,.namba/project/tech.md,.namba/project/mismatch-report.md,.namba/project/quality-report.md, and any relevant.namba/project/systems/*.mdartifacts before drafting the SPEC. - Treat executable code and authoritative config as stronger planning evidence than docs, and preserve code-vs-doc conflicts instead of smoothing them out.
- Keep planning in the current workspace. When branch-per-work is enabled, create or switch to the dedicated
spec/...branch before writing.namba/specs/<SPEC>/. - Use
--current-workspaceonly when the user intentionally wants to scaffold on the current branch without creating a dedicated SPEC branch. - Do not create a planning worktree. Reserve temporary worktrees for overlapping
namba run SPEC-XXX --parallelexecution, and expect them to disappear after the run finishes cleanly. - Create the next sequential
SPEC-XXXpackage under.namba/specs/after that branch decision is explicit. - Seed
.namba/specs/<SPEC>/reviews/with product, engineering, design, and aggregate readiness artifacts. - Unless the invocation includes
--no-review, treat the created SPEC as an automatic handoff and immediately continue with$namba-plan-review SPEC-XXXafter the CLI prints the new SPEC ID. - After the automatic handoff, read
.namba/specs/<SPEC>/reviews/readiness.md; if it does not sayCleared reviews: 3/3, do not treat planning as complete. Continue with$namba-plan-review SPEC-XXXagain, or run the missing$namba-plan-pm-review,$namba-plan-eng-review, and$namba-plan-design-reviewtracks before implementation. - When
--no-reviewis present, stop after scaffold creation and point any later review work to$namba-plan-pm-review,$namba-plan-eng-review, and$namba-plan-design-review, or$namba-plan-review SPEC-XXX. - Keep the scope concrete and implementation-ready.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 42 lines · 16 tokens per session scan A 653f76505aaf
namba-plan is a skill published in the GitHub repository Nam-Cheol/namba-ai (11 stars, last pushed 12d ago), licensed MIT. It adds 16 tokens to every session and 1,160 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
autofix-pr
Watch a pull request and fix it from a worktree of its own - CI failures, review feedback from trusted authors and merge conflicts are fixed, pushed to the PR branch and answered on the PR under the user's git and gh identity. Usage - /autofix-pr [ | ] [--once] [--every ] [--no-reply] [--yes] [instructions]…
simplify
Review recent code changes for reuse, code quality, and efficiency, then directly apply straightforward cleanup improvements. Use when the user wants a post-implementation cleanup pass, pre-PR polish, or asks to simplify/refine recent changes. Invoke with /simplify or /simplify .
review-team
Use when assigned a review or when an authored review boundary is reached.
autofix
Review and repair current local changes until they converge, or run Qwen Code Autofix issue and review workflows from GitHub Actions.
find-simplifications
Use for a periodic repo-wide sweep of qwen-code for accumulated excess surface — dead components and files, orphaned locale keys, exports nothing consumes, added-then-removed scaffolding — filing candidates on a tracking issue and landing only what a maintainer has said yes to. Repo-wide and evidence-first; every…
swarm-pr-review
Run a graph-guided, tool-augmented PR review using context packing, parallel exploration, mandatory repository-agnostic risk-family coverage with dispatch scaled to diff size and risk, independent reviewer validation, critic challenge, and metrics writeback. Use for deep pull request review with low false-positive…